Real-Time System State Prediction & Optimal Rebalancing Strategies for Public Bike Sharing Systems

Public Bicycle Sharing Systems (BSSs) are becoming increasingly popular lately. Rebalancing the system, bringing bikes to empty stations and emptying docks for stations with no available docks, is a critical component of BSS’s operation in order to mitigate system’s reliability and accessibility. The first step for schedule rebalancing is predicting real-time system demand. In this context, the current study developed generalized extreme value (GEV) count models that can predict hourly bike arrivals and departures at each station while accounting for time-of-day, weather, built environment, infrastructure, temporal, and spatial dependency factors. The proposed models were used to analyze the demand patterns in the Capital Bikeshare system and were found to predict the demand at both aggregate and disaggregate levels with reasonable accuracy. Specifically, the total demand in the entire system was predicted within 5% accuracy whereas 90% of the arrival and departure frequency predictions in the next one hour at each station were within a margin of one from the observed frequencies. The second key step for rebalancing is development of an optimal redistribution algorithm to guide a delivery truck, which goes from one station to another, to satisfy individual station demand obtained from the first step. Achieving this objective in an optimal manner (i.e., finding the shortest Hamiltonian cycle) is an NP-hard problem. Heuristic approaches, which can deliver optimal or near optimal solutions at lower computational costs, were proposed. The proposed algorithm was tested using large benchmark instances. The results show promising performance in terms of solution quality and computational time.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; References; Tables;
  • Pagination: 43p

Subject/Index Terms

Filing Info

  • Accession Number: 01689902
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Dec 6 2018 7:45AM